Open-source guarantees the support of the community built behind the technology and eases customizations and third-party extensions. Usage of open-source HW platforms and template architectures, definition of open-source functions and algorithms, and open-source tools and frameworks will facilitate MYRTUS results uptake.

MYRTUS provides an MLIR-based interoperability framework for node level optimization and code generation, facilitating system integration and easing migration among devices, as well as interoperability with third-party solutions (e.g., making the DPE compatible with other tools like PyTorch and TensorFlow). At the orchestration level, an ontological knowledge-based approach, leveraging standards, defines an interoperable hierarchy of distributed monitors and AI-powered managers allowing for cross-layer learning and information flow.

MYRTUS exploits containers-/Kubernetes-based solutions to support portability on already existing systems without changing completely the computing layers. This allows portability over legacy systems, supporting the use of existing infrastructure leveraging principles of the “sharing economy” and pursuing, in turn, sustainability.

Legacy systems compliancy: Part of the MIRTO cognitive engine will be platform dependent, but the formalisation of the cognitive self-adaptation loop, provided as a template based on a set of reusable solutions, and the use of container-based and Kubernetes-based solutions eases implementation and solution porting also on legacy systems. Thanks to these, any substrate can be included or become a MYRTUS-compliant system.